Abstract

A method for estimating multivariate functional relationships between sets of measured oceanographic, meteorological, and other field data is presented. Model II regression is well known for describing functional relationships between two variables. However, there is little accessible guidance for the researcher wishing to apply model II methods to a multivariate system consisting of three or more variables. This paper describes a straightforward method to extend model II regression to the case of three or more variables.

The multiple model II procedure is applied to an analysis of the optical spectral scattering coefficient measured in the coastal ocean. The spectral scattering coefficient is regressed against both suspended mineral particle concentration and suspended organic particle concentration. The regression coefficients from this analysis provide adjusted estimates of the mineral particle scattering cross section and the organic particle scattering cross section. Greater accuracy and efficiency of the coefficients from this analysis, compared to semiempirical coefficients, is demonstrated. Examples of multivariate data are presented that have been analyzed by partitioning the variables into arbitrary bivariate models. However, in a true multivariate system with correlated predictors, such as a coupled biogeochemical cycle, these bivariate analyses yield incorrect coefficient estimates and may result in large unexplained variance. Employing instead a multivariate model II analysis can alleviate these problems and may be a better choice in these situations.

Abstract

Given the rapid increase in the use of operational mesoscale models to satisfy different specialized needs, it is important for the community to share ideas and solutions for meeting the many associated challenges that encompass science, technology, education, and training. As a contribution toward this objective, this paper begins a series that reports on the characteristics and performance of an operational mesogamma-scale weather analysis and forecasting system that has been developed for use by the U.S. Army Test and Evaluation Command. During the more than five years that this four-dimensional weather system has been in use at seven U.S. Army test ranges, valuable experience has been gained about the production and effective use of high-resolution model products for satisfying a variety of needs. This paper serves as a foundation for the rest of the papers in the series by describing the operational requirements for the system, the data assimilation and forecasting system characteristics, and the forecaster training that is required for the finescale products to be used effectively.

The Atmospheric Infrared Sounder (AIRS) and its two companion microwave sounders, AMSU and HSB were launched into polar orbit onboard the NASA Aqua Satellite in May 2002. NASA required the sounding system to provide high-quality research data for climate studies and to meet NOAA's requirements for improving operational weather forecasting. The NOAA requirement translated into global retrieval of temperature and humidity profiles with accuracies approaching those of radiosondes. AIRS also provides new measurements of several greenhouse gases, such as CO₂, CO, CH₄, O₃, SO₂, and aerosols.

The assimilation of AIRS data into operational weather forecasting has already demonstrated significant improvements in global forecast skill. At NOAA/NCEP, the improvement in the forecast skill achieved at 6 days is equivalent to gaining an extension of forecast capability of six hours. This improvement is quite significant when compared to other forecast improvements over the last decade. In addition to NCEP, ECMWF and the Met Office have also reported positive forecast impacts due AIRS.

AIRS is a hyperspectral sounder with 2,378 infrared channels between 3.7 and 15.4 μm. NOAA/NESDIS routinely distributes AIRS data within 3 hours to NWP centers around the world. The AIRS design represents a breakthrough in infrared space instrumentation with measurement stability and accuracies far surpassing any current research or operational sounder..The results we describe in this paper are “work in progress,” and although significant accomplishments have already been made much more work remains in order to realize the full potential of this suite of instruments.

Abstract

Ground-based Doppler-lidar instrumentation provides atmospheric wind data at dramatically improved accuracies and spatial/temporal resolutions. These capabilities have provided new insights into atmospheric flow phenomena, but they also should have a strong role in NWP model improvement. Insight into the nature of model errors can be gained by studying recurrent atmospheric flows, here a regional summertime diurnal sea breeze and subsequent marine-air intrusion into the arid interior of Oregon–Washington, where these winds are an important wind-energy resource. These marine intrusions were sampled by three scanning Doppler lidars in the Columbia River basin as part of the Second Wind Forecast Improvement Project (WFIP2), using data from summer 2016. Lidar time–height cross sections of wind speed identified 8 days when the diurnal flow cycle (peak wind speeds at midnight, afternoon minima) was obvious and strong. The 8-day composite time–height cross sections of lidar wind speeds are used to validate those generated by the operational NCEP–HRRR model. HRRR simulated the diurnal wind cycle, but produced errors in the timing of onset and significant errors due to a premature nighttime demise of the intrusion flow, producing low-bias errors of 6 m s⁻¹. Day-to-day and in the composite, whenever a marine intrusion occurred, HRRR made these same errors. The errors occurred under a range of gradient wind conditions indicating that they resulted from the misrepresentation of physical processes within a limited region around the measurement locations. Because of their generation within a limited geographical area, field measurement programs can be designed to find and address the sources of these NWP errors.

The Community Climate System Model (CCSM) has been created to represent the principal components of the climate system and their interactions. Development and applications of the model are carried out by the U.S. climate research community, thus taking advantage of both wide intellectual participation and computing capabilities beyond those available to most individual U.S. institutions. This article outlines the history of the CCSM, its current capabilities, and plans for its future development and applications, with the goal of providing a summary useful to present and future users.

The initial version of the CCSM included atmosphere and ocean general circulation models, a land surface model that was grafted onto the atmosphere model, a sea-ice model, and a “flux coupler” that facilitates information exchanges among the component models with their differing grids. This version of the model produced a successful 300-yr simulation of the current climate without artificial flux adjustments. The model was then used to perform a coupled simulation in which the atmospheric CO₂ concentration increased by 1 % per year.

In this version of the coupled model, the ocean salinity and deep-ocean temperature slowly drifted away from observed values. A subsequent correction to the roughness length used for sea ice significantly reduced these errors. An updated version of the CCSM was used to perform three simulations of the twentieth century's climate, and several projections of the climate of the twenty-first century.

The CCSM's simulation of the tropical ocean circulation has been significantly improved by reducing the background vertical diffusivity and incorporating an anisotropic horizontal viscosity tensor. The meridional resolution of the ocean model was also refined near the equator. These changes have resulted in a greatly improved simulation of both the Pacific equatorial undercurrent and the surface countercurrents. The interannual variability of the sea surface temperature in the central and eastern tropical Pacific is also more realistic in simulations with the updated model.

Scientific challenges to be addressed with future versions of the CCSM include realistic simulation of the whole atmosphere, including the middle and upper atmosphere, as well as the troposphere; simulation of changes in the chemical composition of the atmosphere through the incorporation of an integrated chemistry model; inclusion of global, prognostic biogeochemical components for land, ocean, and atmosphere; simulations of past climates, including times of extensive continental glaciation as well as times with little or no ice; studies of natural climate variability on seasonal-to-centennial timescales; and investigations of anthropogenic climate change. In order to make such studies possible, work is under way to improve all components of the model. Plans call for a new version of the CCSM to be released in 2002. Planned studies with the CCSM will require much more computer power than is currently available.